Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

Sayakkara, Asanka; Le-Khac, Nhien-An

doi:10.1109/access.2021.3051921

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…EM side-channels that emanate from the device while operating contain sensitive and valuable information about the system and running program [12], [24]. Therefore, EM side-channel signals are used as essential sources to gain insight by many applications that are developed to improve system performance and security [25]- [28].…”

Section: Related Workmentioning

confidence: 99%

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Kerpicci,

Prvulovic,

Zajić

2023

IEEE Access

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This paper proposes a dissimilarity detection algorithm that can find different regions between similar signals. In particular, we address the detection problem of branching statements in program control flow using electromagnetic (EM) side-channels, where we have shown that such statements can be detected in emanating side-channels. Based on the findings, we have proposed a generalized approach for dissimilarity detection that can be efficiently applied to various real-world applications. In the proposed method, symbolic representation of the signals is used for efficient processing, where each signal frame is transformed into a string. The codebook of observed patterns is constructed with the reference signal. Then, the sequence of strings that is obtained from the main signal is compared with the codebook to find the newly observed patterns. Finally, the presented method outputs the samples of dissimilar regions in the signal compared to the reference. In the experiments, various EM side-channel signals are collected from different devices and different control flow examples to show the applicability and efficiency of the proposed method and the results show that dissimilarities can be detected with >98% accuracy. INDEX TERMSDissimilarity detection, electromagnetic side-channel, pattern matching, program control flow, time series representation.

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Section: Related Workmentioning

confidence: 99%

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Kerpicci,

Prvulovic,

Zajić

2023

IEEE Access

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“…Since EM-SCA works when the target DUT is currently powered on, it suits well to the triage examination phase of an investigation. It has been shown experimentally that various forensic insights can be acquired through EM-SCA techniques from embedded devices, such as Arduino and Raspberry Pi, and smartphones [5], [10], [19].…”

Section: Related Workmentioning

confidence: 99%

Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

Sayakkara

Le-Khac

2021

IEEE Access

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Electromagnetic (EM) side-channel radiation from Internet of Things (IoT) devices are shown to be effective at acquiring forensic insights during digital investigations. These EM radiation patterns can be analysed with the help of machine learning algorithms to detect internal behaviours of IoT devices, which can be relevant to an investigation. However, the real-world application of EM sidechannel analysis for digital forensic purposes is obstructed by the lack of suitable tools and the technical expertise among law-enforcement communities. Although certain frameworks, such as EMvidence, exist to cater this requirement, the sheer diversity of the IoT ecosystem makes it difficult to support a sufficiently large collection of devices that are commonly encountered in forensic investigations. The work presented in this paper makes multiple contributions towards addressing this problem. Initially, a detailed discussion on the challenges of applying EM side-channel analysis in practical digital forensic purposes is provided, where the practical difficulties are illustrated. Then, it was shown that the existing EM side-channel analysis frameworks, such as EMvidence, can be used to overcome the diversity of IoT devices in forensics by equipping them with extensible plug-ins targeting the internal system-on-chips (SoC) of each device type. These plug-ins are expected to incorporate trained machine learning models, which are capable of recognising patterns of specific IoT device SoCs. However, the development of such plug-ins requires sufficiently diverse EM datasets from IoT devices. Facilitating this requirement, this work presents a comprehensive EM side-channel dataset representing a diverse collection of popular IoT devices and smartphones. The presented dataset is used to demonstrate the potential usage of machine learning models to recognise device behaviour.

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Ensuring cross-device portability of electromagnetic side-channel analysis for digital forensics

Navanesan,

Le-Khac,

Scanlon

et al. 2024

Forensic Science International: Digital Investigation

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Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

Cited by 5 publications

References 22 publications

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

Ensuring cross-device portability of electromagnetic side-channel analysis for digital forensics

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